Take tour in rmd :

✔️ new file

✔️ CODE CHUNKS

✔️ SET GLOBAL OPTIONS

Set options for the entire document in the first chunk.

✔️ INLINE CODE

Insert 53940 into text sections. Code is evaluated

at render and results appear as text. “Built with r nrow(diamonds)”

✔️ save and render

✔️ see visual editor

✔️ see outline

Insert Tables

Output data frames as tables using kable(data, caption).

data <- faithful[1:4, ]
knitr::kable(data,
caption = "Table with kable")

Write with Markdown

✔️ Plain text.

✔️ End a line with two spaces to start a new paragraph.

✔️ Also end with a backslash\ to make a new line.

✔️ italisc and bold

✔️ superscript^2^/subscript~2~

✔️ ~strikethrough

✔️ escaped: * _ \

✔️ endash: –, emdash: —

✔️ # Header 1

✔️ ## Header 2

✔️ ###### Header 6

✔️ - unordered list

• item 2

• item 2a (indent 1 tab)

• item 2b

✔️ 1. ordered list

2. item 2

• item 2a (indent 1 tab)

• item 2b

✔️ This is a link.

✔️ [This is another link][id].

✔️ verbatim code

✔️ multiple lines

 of verbatim code

✔️ > block quotes

✔️equation: $e^{i /pi} + 1 = 0$

✔️equation block:

✔️ $$E = mc^{2}$$

HTML Tabsets

output

+==========================================================+ +———————————————————-+

params

1. Add parameters in the header as sub-values of params.
2. Call parameters in code using params$<name>.
3. Set parameters with Knit with Parameters or the params argument of render().

REUSABLE TEMPLATES

1. Create a new package with a inst/rmarkdown/

templates directory.

2. Add a folder containing template.yaml (below)

and skeleton.Rmd (template contents).

name: “My Template”

3. Install the package to access template by going to

File > New R Markdown > From Template.

BOOTSWATCH THEMES

Customize HTML documents with Bootswatch themes from the bslib package using the theme

output option.Use bslib::bootswatch_themes() to list available themes.

CUSTOM THEMES

Customize individual HTML elements using bslib

variables. Use ?bs_theme to see more variables.

More on bslib at pkgs.rstudio.com/bslib/.

STYLING WITH CSS AND SCSS

Add CSS and SCSS to your document by adding a

path to a file with the css option in the YAML header.

Apply CSS styling by writing HTML tags directly or:

• Use markdown to apply style attributes inline.

Render

When you render a document, rmarkdown:

1. Runs the code and embeds results and text into an .md file with knitr.

2. Converts the .md file into the output format with Pandoc.

Save, then Knit to preview the document output.

The resulting HTML/PDF/MS Word/etc. document will be created and saved in the same directory as

the .Rmd file.Use rmarkdown::render() to render/knit in the R

console. See ?render for available options.

Share

Publish on RStudio Connectto share R Markdown documents

securely, schedule automaticupdates, and interact with parameters in real time.

rstudio.com/products/connect/

INTERACTIVITY

1. Add runtime: shiny to the YAML header.

2. Call Shiny input functions to embed input objects.

3. Call Shiny render functions to embed reactiveoutput.

4. Render with rmarkdown::run() or click Run Document in RStudio IDE.

Bookdown

Maybe you want to write a technical book, or maybe your paper/write-up is so big that you need to split it into chapters. bookdown is an R package which allows you to construct a book structure to your output. You can write your chapters in separate R Markdown files headed with # level headings. You can employ an easy reference format to reference a bibliography or other other sections, chapters, figures or tables. You can then render the entire book in some neat HTML formats like Gitbook or Bootstrap, or you can render it as a pdf or epub format. Here’s an example of a recent book I wrote in Gitbook and in Bootstrap 4 (development version of bookdown). More on bookdown here.

#library(bookdown)
#library(downlit)
#bookdown::bs4_book()

#browseURL("https://bookdown.org/yihui/rmarkdown-cookbook/notebook.html")

interactive graphics

Use interactive graphics in your documents by embedding `plotly`

It’s a really effective teaching tool to allow your readers to interact

with your data or graphics as part of your R markdown documents.

Personally I love `plotly` for generating interactive graphics in 2D and

3D. You can insert plotly code into a code chunk in an R Markdown

document (it can be coded in R or Python — see Point 3), and this will

generate a beautiful graphic that the reader can interact with to see

data points, rotate, or whatever. Here’s an example. More on `plotly`

here.](https://plotly.com/r/).)

library(plotly)

library(gapminder)

p <- gapminder %>%
  
    filter(year==1977) %>%
  
    ggplot( aes(gdpPercap, lifeExp, size = pop, color=continent)) +
  
    geom_point() +
  
    scale_x_log10() +
  
    theme_bw()



ggplotly(p)

library(DT)

datatable(mtcars, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )

Presentations

Presentations work by dividing your content into slides, with a new slide beginning at each first (#) or second (##) level header. You can also insert a horizontal rule (***) to create a new slide without a header. R Markdown comes with three presentation formats built-in:

1. ioslides_presentation - HTML presentation with ioslides

2. slidy_presentation - HTML presentation with W3C Slidy

3. beamer_presentation - PDF presentation with LaTeX Beamer.

   Dashboards

   Dashboards are a useful way to communicate large amounts of information visually and quickly. Flexdashboard makes it particularly easy to create dashboards using R Markdown and a convention for how the headers affect the layout:

   • Each level 1 header (#) begins a new page in the dashboard.

   • Each level 2 header (##) begins a new column.

   • Each level 3 header (###) begins a new row.

# ---
# title: "Diamonds distribution dashboard"
# output: flexdashboard::flex_dashboard
# ---
# 
# library(ggplot2)
# library(dplyr)
# knitr::opts_chunk$set(fig.width = 5, fig.asp = 1/3)
# 
#  ## Column 1
# ### Carat
#  ggplot(diamonds, aes(carat)) + geom_histogram(binwidth = 0.1)
# 
# ### Cut
# ggplot(diamonds, aes(cut)) + geom_bar()
# ### Colour
# ggplot(diamonds, aes(color)) + geom_bar()
# ## Column 2
# ### The largest diamonds
#             diamonds %>% 
#               arrange(desc(carat)) %>% 
#               head(100) %>% 
#               select(carat, cut, color, price) %>% 
#               DT::datatable()

Shiny

theme_set(theme_minimal())

ui <- fluidPage(
  #Create input for domain (single) and variable (multiple)
  selectInput("domain", "Domain", choices = ""),
  selectInput("varSelection", "Choose 2 or 3 variables", multiple = T, choices = ""),
  
  #Set plot for output
  plotOutput("myPlot")
)

server <- function(input, output, session) {
  
  #Load your data
  df <- tibble(d = c(1,1,2,2),
               year = c(2015, 2016, 2015, 2016),
               v1 = c(3,5,4,10),
               v2 = c(7,11,13,18),
               v3 = c(1,2,3,4))
  
  #Update the domain input according to the data
  updateSelectInput(session, "domain", choices = sort(unique(df$d)))
  
  #Update the variable list (assumed all but d and year are variables of interest)
  updateSelectInput(session, "varSelection", 
                    choices = colnames(df %>% select(-d, -year)))
  
  #Load the chart function
  draw_chart <- function(df, listv, d){
    df2 <- df %>%
      gather("variable", "value", 3:5)
    df3 <- df2 %>%
      filter(variable %in% listv)
    
    df4 <- df3 %>%
      group_by(d, year) %>%
      summarise(value = mean(value)) %>%
      mutate(variable = "m")
    
    df5 <- bind_rows(df3, df4) 
    
    df5 <- df5 %>%
      mutate(year = as.character(year)) %>%
      mutate(year = as.Date(year, "%Y"))
    
    df5 <- df5 %>%
      mutate(year = lubridate::year(year))
    
    df5 <- df5 %>%
      filter(d == 1)
    # format(df5$year, "%Y")
    # Visualization
    ggplot(df5, aes(x = year, y = value)) + 
      geom_line(aes(color = variable, linetype = variable)) + 
      scale_color_discrete() +
      scale_x_continuous(breaks = c(2015,2016))
  }
  
  #Render the plot
  output$myPlot = renderPlot({
    #Only render if there are 2 or 3 variables selected
    req(between(length(input$varSelection), 2, 3))
    draw_chart(df, input$varSelection, input$domain)
  })
  
}

shinyApp(ui, server)

Reproducable search

• replication of my study

**An investigator at another institution conducts a study addressing the same question, collects her own data, analyzes it separately from me, and publishes her own findings.**

• reproducible :

  The full computer code for doing the data analysis is made publicly available

• Which of the following is a reason that a study might NOT be fully replicated?

  he original study was very expensive and there is no money to repeat it in a different setting.

• Which of the following is a reason why publishing reproducible research is increasingly important?

  New technologies are increasing the rate of data collection, creating datasets that are more complex and extremely high dimensional.